Influence of non-dipole field on determination of Plio-Pleistocene true polar wander

Some studies of global paleomagnetic data have found an offset of the magnetic pole during the Plio-Pleistocene which has been interpreted as indicating a period of rapid True Polar Wander, with a rate of movement comparable to the present-day rate of polar motion deduced from astronomical observations. We show that much of the polar offset determined from the paleomagnetic data may be due to deviations in pole position caused by persistent non-dipole zonal components of the geomagnetic field. A correction of paleomagnetic poles for the long-term non-dipole field reduces the polar offset and thus suggests a slower or shorter episode of True Polar Wander over the past 5 million years

Inclination anomalies from Indian Ocean sediments and the possibility of a standing non-dipole field

We report magnetic inclinations measured in deep-sea sediments of the equatorial Indian Ocean which record the behavior of a nondipole component of the timeaveraged geomagnetic field during the Plio-Pleistocene (0-5 Ma). The magnitude of the nondipole effect recorded in these sediments appears to depend on polarity state, with inclinations showing departures from geocentric axial dipole directions which are small (2°) during normal polarity and larger (5°) during reverse polarity times. The overall nondipole effect observed here is consistent with prior spherical harmonic estimates of the paleomagnetic field; the polarity bias found agrees, in both sense and magnitude, with earlier reports of polarity asymmetry in the low-degree zonal harmonic fields. The presence of this asymmetry supports previous suggestions of the existence of a standing component of the nondipole field which does not invert during reversals of the main field. We explore whether the standing field so indicated may have influenced paleomagnetic directions recorded during polarity transitions at other equatorial sites

Calibration of PCB-132 Sensors in a Shock Tube

While PCB-132 sensors have proven useful for measuring second-mode instability waves in many hypersonic wind tunnels, they are currently limited by their calibration. Until now, the factory calibration has been all that was available, which is a single-point calibration at an amplitude three orders of magnitude higher than a second-mode wave. In addition, little information has been available about the frequency response or spatial resolution of the sensors, which is important for measuring high-frequency instability waves. These shortcomings make it difficult to compare measurements at different conditions and between different sensors. If accurate quantitative measurements could be performed, comparisons of the growth and breakdown of instability waves could be made in different facilities, possibly leading to a method of predicting the amplitude at which the waves break down into turbulence, improving transition prediction. A method for calibrating the sensors is proposed using a newly-built shock tube at Purdue University. This shock tube, essentially a half-scale version of the 6-Inch shock tube at the Graduate Aerospace Laboratories at Caltech, has been designed to attain a moderate vacuum in the driven section. Low driven pressures should allow the creation of very weak, yet still relatively thin shock waves. It is expected that static pressure rises within the range of second-mode amplitudes should be possible. The shock tube has been designed to create clean, planar shock waves with a laminar boundary layer to allow for accurate calibrations. Stronger shock waves can be used to identify the frequency response of the sensors out to hundreds of kilohertz

Efficient Domain Adaptation of Sentence Embeddings using Adapters

Sentence embeddings enable us to capture the semantic similarity of short texts. Most sentence embedding models are trained for general semantic textual similarity (STS) tasks. Therefore, to use sentence embeddings in a particular domain, the model must be adapted to it in order to achieve good results. Usually, this is done by fine-tuning the entire sentence embedding model for the domain of interest. While this approach yields state-of-the-art results, all of the model's weights are updated during fine-tuning, making this method resource-intensive. Therefore, instead of fine-tuning entire sentence embedding models for each target domain individually, we propose to train lightweight adapters. These domain-specific adapters do not require fine-tuning all underlying sentence embedding model parameters. Instead, we only train a small number of additional parameters while keeping the weights of the underlying sentence embedding model fixed. Training domain-specific adapters allows always using the same base model and only exchanging the domain-specific adapters to adapt sentence embeddings to a specific domain. We show that using adapters for parameter-efficient domain adaptation of sentence embeddings yields competitive performance within 1% of a domain-adapted, entirely fine-tuned sentence embedding model while only training approximately 3.6% of the parameters.Comment: Accepted to the International Conference on Recent Advances in Natural Language Processing (RANLP 2023

Multi-Elemental Analysis of Sandhills Meadow Hay

Traditionally, ranches in the Sandhills of Nebraska feed meadow hay to cows during the winter. Trace element composition of meadow hay varies. To determine if, when and where trace element supplementation is necessary, it is important to characterize the trace element concentrations in the hay. If the variation in trace element content of hay among location and years can be predicted ranch managers and advisors may develop appropriate strategies for sampling hay and preventing trace element deficiencies. Our objective was to develop a data base of trace element content of meadow hay from various locations over two years. The data base could be used to predict needs for sampling and supplementation strategies. appropriate strategies for sampling and supplementation strategies